



"""simple docstring"""






import shutil
import tempfile
import unittest

from transformers import SPIECE_UNDERLINE, BatchEncoding, MBartaaTokenizer, MBartaaTokenizerFast, is_torch_available
from transformers.testing_utils import (
    get_tests_dir,
    nested_simplify,
    require_sentencepiece,
    require_tokenizers,
    require_torch,
    slow,
)

from ...test_tokenization_common import TokenizerTesterMixin


snake_case   					=				get_tests_dir('''fixtures/test_sentencepiece.model''')

if is_torch_available():
     from transformers.models.mbart.modeling_mbart import shift_tokens_right

snake_case   					=				2_5_0_0_0_4
snake_case   					=				2_5_0_0_2_0



@require_sentencepiece
@require_tokenizers
class     UpperCAmelCase    (	__SCREAMING_SNAKE_CASE,unittest.TestCase  ):
   A__		:	Tuple           =  MBartaaTokenizer
   A__		:	Dict           =  MBartaaTokenizerFast
   A__		:	int           =  True
   A__		:	str           =  True



   def    __UpperCAmelCase    (       self    :	str				):



     """simple docstring"""
     super().setUp()

     # We have a SentencePiece fixture for testing
     _snake_case   	=      MBartaaTokenizer(__lowerCamelCase							,       src_lang='''en_XX'''							,       tgt_lang='''ro_RO'''							,       keep_accents=__lowerCamelCase				)
     tokenizer.save_pretrained(self.tmpdirname				)



   def    __UpperCAmelCase    (       self    :	str				):



     """simple docstring"""
     _snake_case   	=      '''<s>'''
     _snake_case   	=      0

     self.assertEqual(self.get_tokenizer()._convert_token_to_id(__lowerCamelCase				)							,       __lowerCamelCase				)
     self.assertEqual(self.get_tokenizer()._convert_id_to_token(__lowerCamelCase				)							,       __lowerCamelCase				)



   def    __UpperCAmelCase    (       self    :	List[Any]				):



     """simple docstring"""
     _snake_case   	=      list(self.get_tokenizer().get_vocab().keys()				)

     self.assertEqual(vocab_keys[0]							,       '''<s>'''				)
     self.assertEqual(vocab_keys[1]							,       '''<pad>'''				)
     self.assertEqual(vocab_keys[-1]							,       '''<mask>'''				)
     self.assertEqual(len(__lowerCamelCase				)							,       1_0_5_4				)



   def    __UpperCAmelCase    (       self    :	Union[str, Any]				):



     """simple docstring"""
     self.assertEqual(self.get_tokenizer().vocab_size							,       1_0_5_4				)



   def    __UpperCAmelCase    (       self    :	Tuple				):



     """simple docstring"""
     _snake_case   	=      MBartaaTokenizer(__lowerCamelCase							,       src_lang='''en_XX'''							,       tgt_lang='''ro_RO'''							,       keep_accents=__lowerCamelCase				)

     _snake_case   	=      tokenizer.tokenize('''This is a test'''				)
     self.assertListEqual(__lowerCamelCase							,       ['''▁This''', '''▁is''', '''▁a''', '''▁t''', '''est''']				)

     self.assertListEqual(
         tokenizer.convert_tokens_to_ids(__lowerCamelCase				)							,       [value + tokenizer.fairseq_offset for value in [2_8_5, 4_6, 1_0, 1_7_0, 3_8_2]]							,       )

     _snake_case   	=      tokenizer.tokenize('''I was born in 92000, and this is falsé.'''				)
     self.assertListEqual(
         __lowerCamelCase							,       [SPIECE_UNDERLINE + '''I''', SPIECE_UNDERLINE + '''was''', SPIECE_UNDERLINE + '''b''', '''or''', '''n''', SPIECE_UNDERLINE + '''in''', SPIECE_UNDERLINE + '''''', '''9''', '''2''', '''0''', '''0''', '''0''', ''',''', SPIECE_UNDERLINE + '''and''', SPIECE_UNDERLINE + '''this''', SPIECE_UNDERLINE + '''is''', SPIECE_UNDERLINE + '''f''', '''al''', '''s''', '''é''', '''.''']							,       )
     _snake_case   	=      tokenizer.convert_tokens_to_ids(__lowerCamelCase				)
     self.assertListEqual(
         __lowerCamelCase							,       [
             value + tokenizer.fairseq_offset
             for value in [8, 2_1, 8_4, 5_5, 2_4, 1_9, 7, 2, 6_0_2, 3_4_7, 3_4_7, 3_4_7, 3, 1_2, 6_6, 4_6, 7_2, 8_0, 6, 2, 4]
         ]							,       )

     _snake_case   	=      tokenizer.convert_ids_to_tokens(__lowerCamelCase				)
     self.assertListEqual(
         __lowerCamelCase							,       [SPIECE_UNDERLINE + '''I''', SPIECE_UNDERLINE + '''was''', SPIECE_UNDERLINE + '''b''', '''or''', '''n''', SPIECE_UNDERLINE + '''in''', SPIECE_UNDERLINE + '''''', '''<unk>''', '''2''', '''0''', '''0''', '''0''', ''',''', SPIECE_UNDERLINE + '''and''', SPIECE_UNDERLINE + '''this''', SPIECE_UNDERLINE + '''is''', SPIECE_UNDERLINE + '''f''', '''al''', '''s''', '''<unk>''', '''.''']							,       )



   @slow
   def    __UpperCAmelCase    (       self    :	Tuple				):



     """simple docstring"""
     # fmt: off
     _snake_case   	=      {'''input_ids''': [[2_5_0_0_0_4, 1_1_0_6_2, 8_2_7_7_2, 7, 1_5, 8_2_7_7_2, 5_3_8, 5_1_5_2_9, 2_3_7, 1_7_1_9_8, 1_2_9_0, 2_0_6, 9, 2_1_5_1_7_5, 1_3_1_4, 1_3_6, 1_7_1_9_8, 1_2_9_0, 2_0_6, 9, 5_6_3_5_9, 4_2, 1_2_2_0_0_9, 9, 1_6_4_6_6, 1_6, 8_7_3_4_4, 4_5_3_7, 9, 4_7_1_7, 7_8_3_8_1, 6, 1_5_9_9_5_8, 7, 1_5, 2_4_4_8_0, 6_1_8, 4, 5_2_7, 2_2_6_9_3, 5_4_2_8, 4, 2_7_7_7, 2_4_4_8_0, 9_8_7_4, 4, 4_3_5_2_3, 5_9_4, 4, 8_0_3, 1_8_3_9_2, 3_3_1_8_9, 1_8, 4, 4_3_5_2_3, 2_4_4_4_7, 1_2_3_9_9, 1_0_0, 2_4_9_5_5, 8_3_6_5_8, 9_6_2_6, 1_4_4_0_5_7, 1_5, 8_3_9, 2_2_3_3_5, 1_6, 1_3_6, 2_4_9_5_5, 8_3_6_5_8, 8_3_4_7_9, 1_5, 3_9_1_0_2, 7_2_4, 1_6, 6_7_8, 6_4_5, 2_7_8_9, 1_3_2_8, 4_5_8_9, 4_2, 1_2_2_0_0_9, 1_1_5_7_7_4, 2_3, 8_0_5, 1_3_2_8, 4_6_8_7_6, 7, 1_3_6, 5_3_8_9_4, 1_9_4_0, 4_2_2_2_7, 4_1_1_5_9, 1_7_7_2_1, 8_2_3, 4_2_5, 4, 2_7_5_1_2, 9_8_7_2_2, 2_0_6, 1_3_6, 5_5_3_1, 4_9_7_0, 9_1_9, 1_7_3_3_6, 5, 2], [2_5_0_0_0_4, 2_0_0_8_0, 6_1_8, 8_3, 8_2_7_7_5, 4_7, 4_7_9, 9, 1_5_1_7, 7_3, 5_3_8_9_4, 3_3_3, 8_0_5_8_1, 1_1_0_1_1_7, 1_8_8_1_1, 5_2_5_6, 1_2_9_5, 5_1, 1_5_2_5_2_6, 2_9_7, 7_9_8_6, 3_9_0, 1_2_4_4_1_6, 5_3_8, 3_5_4_3_1, 2_1_4, 9_8, 1_5_0_4_4, 2_5_7_3_7, 1_3_6, 7_1_0_8, 4_3_7_0_1, 2_3, 7_5_6, 1_3_5_3_5_5, 7, 5, 2, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1], [2_5_0_0_0_4, 5_8_1, 6_3_7_7_3, 1_1_9_4_5_5, 6, 1_4_7_7_9_7, 8_8_2_0_3, 7, 6_4_5, 7_0, 2_1, 3_2_8_5, 1_0_2_6_9, 5, 2, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1]], '''attention_mask''': [[1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1], [1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0]]}  # noqa: E501
     # fmt: on

     self.tokenizer_integration_test_util(
         expected_encoding=__lowerCamelCase							,       model_name='''facebook/mbart-large-50'''							,       revision='''d3913889c59cd5c9e456b269c376325eabad57e2'''							,       )





   def    __UpperCAmelCase    (       self    :	int				):



     """simple docstring"""
     if not self.test_slow_tokenizer:
       # as we don't have a slow version, we can't compare the outputs between slow and fast versions
       return

     _snake_case   	=      (self.rust_tokenizer_class, '''hf-internal-testing/tiny-random-mbart50''', {})
     for tokenizer, pretrained_name, kwargs in self.tokenizers_list:
       with self.subTest(f"""{tokenizer.__class__.__name__} ({pretrained_name})"""				):
         _snake_case   	=      self.rust_tokenizer_class.from_pretrained(__lowerCamelCase							,       **__lowerCamelCase				)
         _snake_case   	=      self.tokenizer_class.from_pretrained(__lowerCamelCase							,       **__lowerCamelCase				)

         _snake_case   	=      tempfile.mkdtemp()

         _snake_case   	=      tokenizer_r.save_pretrained(__lowerCamelCase				)
         _snake_case   	=      tokenizer_p.save_pretrained(__lowerCamelCase				)

         # Checks it save with the same files + the tokenizer.json file for the fast one
         self.assertTrue(any('''tokenizer.json''' in f for f in tokenizer_r_files				)				)
         _snake_case   	=      tuple(f for f in tokenizer_r_files if '''tokenizer.json''' not in f				)
         self.assertSequenceEqual(__lowerCamelCase							,       __lowerCamelCase				)

         # Checks everything loads correctly in the same way
         _snake_case   	=      tokenizer_r.from_pretrained(__lowerCamelCase				)
         _snake_case   	=      tokenizer_p.from_pretrained(__lowerCamelCase				)

         # Check special tokens are set accordingly on Rust and Python
         for key in tokenizer_pp.special_tokens_map:
           self.assertTrue(hasattr(__lowerCamelCase							,       __lowerCamelCase				)				)
           # self.assertEqual(getattr(tokenizer_rp, key), getattr(tokenizer_pp, key))
           # self.assertEqual(getattr(tokenizer_rp, key + "_id"), getattr(tokenizer_pp, key + "_id"))

         shutil.rmtree(__lowerCamelCase				)

         # Save tokenizer rust, legacy_format=True
         _snake_case   	=      tempfile.mkdtemp()

         _snake_case   	=      tokenizer_r.save_pretrained(__lowerCamelCase							,       legacy_format=__lowerCamelCase				)
         _snake_case   	=      tokenizer_p.save_pretrained(__lowerCamelCase				)

         # Checks it save with the same files
         self.assertSequenceEqual(__lowerCamelCase							,       __lowerCamelCase				)

         # Checks everything loads correctly in the same way
         _snake_case   	=      tokenizer_r.from_pretrained(__lowerCamelCase				)
         _snake_case   	=      tokenizer_p.from_pretrained(__lowerCamelCase				)

         # Check special tokens are set accordingly on Rust and Python
         for key in tokenizer_pp.special_tokens_map:
           self.assertTrue(hasattr(__lowerCamelCase							,       __lowerCamelCase				)				)

         shutil.rmtree(__lowerCamelCase				)

         # Save tokenizer rust, legacy_format=False
         _snake_case   	=      tempfile.mkdtemp()

         _snake_case   	=      tokenizer_r.save_pretrained(__lowerCamelCase							,       legacy_format=__lowerCamelCase				)
         _snake_case   	=      tokenizer_p.save_pretrained(__lowerCamelCase				)

         # Checks it saved the tokenizer.json file
         self.assertTrue(any('''tokenizer.json''' in f for f in tokenizer_r_files				)				)

         # Checks everything loads correctly in the same way
         _snake_case   	=      tokenizer_r.from_pretrained(__lowerCamelCase				)
         _snake_case   	=      tokenizer_p.from_pretrained(__lowerCamelCase				)

         # Check special tokens are set accordingly on Rust and Python
         for key in tokenizer_pp.special_tokens_map:
           self.assertTrue(hasattr(__lowerCamelCase							,       __lowerCamelCase				)				)

         shutil.rmtree(__lowerCamelCase				)



@require_torch
@require_sentencepiece
@require_tokenizers
class     UpperCAmelCase    (	unittest.TestCase  ):
   A__		:	Dict           =  '''facebook/mbart-large-50-one-to-many-mmt'''
   A__		:	Any           =  [
       ''' UN Chief Says There Is No Military Solution in Syria''',
       ''' Secretary-General Ban Ki-moon says his response to Russia\'s stepped up military support for Syria is that "there is no military solution" to the nearly five-year conflict and more weapons will only worsen the violence and misery for millions of people.''',
   ]
   A__		:	Dict           =  [
       '''Şeful ONU declară că nu există o soluţie militară în Siria''',
       '''Secretarul General Ban Ki-moon declară că răspunsul său la intensificarea sprijinului militar al Rusiei'''
       ''' pentru Siria este că "nu există o soluţie militară" la conflictul de aproape cinci ani şi că noi arme nu vor'''
       ''' face decât să înrăutăţească violenţele şi mizeria pentru milioane de oameni.''',
   ]
   A__		:	Optional[Any]           =  [EN_CODE, 8274, 127873, 25916, 7, 8622, 2071, 438, 67485, 53, 187895, 23, 51712, 2]



   @classmethod
   def    __UpperCAmelCase    (       cls    :	List[str]				):



     """simple docstring"""
     _snake_case   	=      MBartaaTokenizer.from_pretrained(
         cls.checkpoint_name							,       src_lang='''en_XX'''							,       tgt_lang='''ro_RO'''				)
     _snake_case   	=      1
     return cls



   def    __UpperCAmelCase    (       self    :	int				):



     """simple docstring"""
     self.assertEqual(self.tokenizer.fairseq_tokens_to_ids['''ar_AR''']							,       2_5_0_0_0_1				)
     self.assertEqual(self.tokenizer.fairseq_tokens_to_ids['''en_EN''']							,       2_5_0_0_0_4				)
     self.assertEqual(self.tokenizer.fairseq_tokens_to_ids['''ro_RO''']							,       2_5_0_0_2_0				)
     self.assertEqual(self.tokenizer.fairseq_tokens_to_ids['''mr_IN''']							,       2_5_0_0_3_8				)



   def    __UpperCAmelCase    (       self    :	str				):



     """simple docstring"""
     _snake_case   	=      self.tokenizer.batch_encode_plus(self.src_text				).input_ids[0]
     self.assertListEqual(self.expected_src_tokens							,       __lowerCamelCase				)



   def    __UpperCAmelCase    (       self    :	Any				):



     """simple docstring"""
     self.assertIn(__lowerCamelCase							,       self.tokenizer.all_special_ids				)
     _snake_case   	=      [RO_CODE, 8_8_4, 9_0_1_9, 9_6, 9, 9_1_6, 8_6_7_9_2, 3_6, 1_8_7_4_3, 1_5_5_9_6, 5, 2]
     _snake_case   	=      self.tokenizer.decode(__lowerCamelCase							,       skip_special_tokens=__lowerCamelCase				)
     _snake_case   	=      self.tokenizer.decode(generated_ids[1:]							,       skip_special_tokens=__lowerCamelCase				)
     self.assertEqual(__lowerCamelCase							,       __lowerCamelCase				)
     self.assertNotIn(self.tokenizer.eos_token							,       __lowerCamelCase				)



   def    __UpperCAmelCase    (       self    :	int				):



     """simple docstring"""
     _snake_case   	=      ['''this is gunna be a long sentence ''' * 2_0]
     assert isinstance(src_text[0]							,       __lowerCamelCase				)
     _snake_case   	=      1_0
     _snake_case   	=      self.tokenizer(__lowerCamelCase							,       max_length=__lowerCamelCase							,       truncation=__lowerCamelCase				).input_ids[0]
     self.assertEqual(ids[0]							,       __lowerCamelCase				)
     self.assertEqual(ids[-1]							,       2				)
     self.assertEqual(len(__lowerCamelCase				)							,       __lowerCamelCase				)



   def    __UpperCAmelCase    (       self    :	str				):



     """simple docstring"""
     self.assertListEqual(self.tokenizer.convert_tokens_to_ids(['''<mask>''', '''ar_AR''']				)							,       [2_5_0_0_5_3, 2_5_0_0_0_1]				)



   def    __UpperCAmelCase    (       self    :	List[Any]				):



     """simple docstring"""
     _snake_case   	=      tempfile.mkdtemp()
     _snake_case   	=      self.tokenizer.fairseq_tokens_to_ids
     self.tokenizer.save_pretrained(__lowerCamelCase				)
     _snake_case   	=      MBartaaTokenizer.from_pretrained(__lowerCamelCase				)
     self.assertDictEqual(new_tok.fairseq_tokens_to_ids							,       __lowerCamelCase				)



   @require_torch
   def    __UpperCAmelCase    (       self    :	Optional[Any]				):



     """simple docstring"""
     _snake_case   	=      self.tokenizer(self.src_text							,       text_target=self.tgt_text							,       padding=__lowerCamelCase							,       return_tensors='''pt'''				)
     _snake_case   	=      shift_tokens_right(batch['''labels''']							,       self.tokenizer.pad_token_id				)

     # fairseq batch: https://gist.github.com/sshleifer/cba08bc2109361a74ac3760a7e30e4f4
     assert batch.input_ids[1][0] == EN_CODE
     assert batch.input_ids[1][-1] == 2
     assert batch.labels[1][0] == RO_CODE
     assert batch.labels[1][-1] == 2
     assert batch.decoder_input_ids[1][:2].tolist() == [2, RO_CODE]



   @require_torch
   def    __UpperCAmelCase    (       self    :	int				):



     """simple docstring"""
     _snake_case   	=      self.tokenizer(
         self.src_text							,       text_target=self.tgt_text							,       padding=__lowerCamelCase							,       truncation=__lowerCamelCase							,       max_length=len(self.expected_src_tokens				)							,       return_tensors='''pt'''							,       )
     _snake_case   	=      shift_tokens_right(batch['''labels''']							,       self.tokenizer.pad_token_id				)

     self.assertIsInstance(__lowerCamelCase							,       __lowerCamelCase				)

     self.assertEqual((2, 1_4)							,       batch.input_ids.shape				)
     self.assertEqual((2, 1_4)							,       batch.attention_mask.shape				)
     _snake_case   	=      batch.input_ids.tolist()[0]
     self.assertListEqual(self.expected_src_tokens							,       __lowerCamelCase				)
     self.assertEqual(2							,       batch.decoder_input_ids[0, 0]				)  # decoder_start_token_id
     # Test that special tokens are reset
     self.assertEqual(self.tokenizer.prefix_tokens							,       [EN_CODE]				)
     self.assertEqual(self.tokenizer.suffix_tokens							,       [self.tokenizer.eos_token_id]				)



   def    __UpperCAmelCase    (       self    :	List[str]				):



     """simple docstring"""
     _snake_case   	=      self.tokenizer(self.src_text							,       padding=__lowerCamelCase							,       truncation=__lowerCamelCase							,       max_length=3							,       return_tensors='''pt'''				)
     _snake_case   	=      self.tokenizer(
         text_target=self.tgt_text							,       padding=__lowerCamelCase							,       truncation=__lowerCamelCase							,       max_length=1_0							,       return_tensors='''pt'''				)
     _snake_case   	=      targets['''input_ids''']
     _snake_case   	=      shift_tokens_right(__lowerCamelCase							,       self.tokenizer.pad_token_id				)

     self.assertEqual(batch.input_ids.shape[1]							,       3				)
     self.assertEqual(batch.decoder_input_ids.shape[1]							,       1_0				)





   @require_torch
   def    __UpperCAmelCase    (       self    :	List[str]				):



     """simple docstring"""
     _snake_case   	=      self.tokenizer._build_translation_inputs(
         '''A test'''							,       return_tensors='''pt'''							,       src_lang='''en_XX'''							,       tgt_lang='''ar_AR'''				)

     self.assertEqual(
         nested_simplify(__lowerCamelCase				)							,       {
             # en_XX, A, test, EOS
             '''input_ids''': [[2_5_0_0_0_4, 6_2, 3_0_3_4, 2]],
             '''attention_mask''': [[1, 1, 1, 1]],
             # ar_AR
             '''forced_bos_token_id''': 2_5_0_0_0_1,
         }							,       )







import torch

from diffusers import CMStochasticIterativeScheduler

from .test_schedulers import SchedulerCommonTest

class 							_UpperCamelCase					(       _A	):






			'''simple docstring'''

			__UpperCamelCase   :    Tuple          =			(CMStochasticIterativeScheduler,)
			__UpperCamelCase   :    List[str]          =			10

			def    lowerCAmelCase__						(							self			:							Optional[int]    ,						**snake_case_			:							List[str]   ):
							UpperCamelCase_:				Optional[int]						=    {
							    """num_train_timesteps""": 201,
							    """sigma_min""": 0.002,
							    """sigma_max""": 80.0,
							}

							config.update(**snake_case_   )
							return config

			def    lowerCAmelCase__						(							self			:							Tuple   ):
							UpperCamelCase_:				Tuple						=    10

							UpperCamelCase_:				Tuple						=    self.get_scheduler_config()
							UpperCamelCase_:				List[str]						=    self.scheduler_classes[0](**snake_case_   )

							scheduler.set_timesteps(snake_case_   )

							UpperCamelCase_:				int						=    scheduler.timesteps[0]
							UpperCamelCase_:				str						=    scheduler.timesteps[1]

							UpperCamelCase_:				Optional[int]						=    self.dummy_sample
							UpperCamelCase_:				int						=    0.1 * sample

							UpperCamelCase_:				Optional[Any]						=    scheduler.step(snake_case_    ,						snake_case_    ,						snake_case_   ).prev_sample
							UpperCamelCase_:				Any						=    scheduler.step(snake_case_    ,						snake_case_    ,						snake_case_   ).prev_sample

							self.assertEqual(output_a.shape    ,						sample.shape   )
							self.assertEqual(output_a.shape    ,						output_a.shape   )

			def    lowerCAmelCase__						(							self			:							Optional[Any]   ):
							for timesteps in [10, 50, 100, 1000]:
											self.check_over_configs(num_train_timesteps=snake_case_   )

			def    lowerCAmelCase__						(							self			:							Optional[Any]   ):
							for clip_denoised in [True, False]:
											self.check_over_configs(clip_denoised=snake_case_   )

			def    lowerCAmelCase__						(							self			:							Any   ):
							UpperCamelCase_:				Any						=    self.scheduler_classes[0]
							UpperCamelCase_:				List[str]						=    self.get_scheduler_config()
							UpperCamelCase_:				Tuple						=    scheduler_class(**snake_case_   )

							UpperCamelCase_:				Optional[int]						=    1
							scheduler.set_timesteps(snake_case_   )
							UpperCamelCase_:				Any						=    scheduler.timesteps

							UpperCamelCase_:				int						=    torch.manual_seed(0   )

							UpperCamelCase_:				Any						=    self.dummy_model()
							UpperCamelCase_:				Union[str, Any]						=    self.dummy_sample_deter * scheduler.init_noise_sigma

							for i, t in enumerate(snake_case_   ):
											# 1. scale model input
											UpperCamelCase_:				str						=    scheduler.scale_model_input(snake_case_    ,						snake_case_   )

											# 2. predict noise residual
											UpperCamelCase_:				List[Any]						=    model(snake_case_    ,						snake_case_   )

											# 3. predict previous sample x_t-1
											UpperCamelCase_:				str						=    scheduler.step(snake_case_    ,						snake_case_    ,						snake_case_    ,						generator=snake_case_   ).prev_sample

											UpperCamelCase_:				Union[str, Any]						=    pred_prev_sample

							UpperCamelCase_:				int						=    torch.sum(torch.abs(snake_case_   )   )
							UpperCamelCase_:				List[Any]						=    torch.mean(torch.abs(snake_case_   )   )

							assert abs(result_sum.item() - 192.7614   ) < 1e-2
							assert abs(result_mean.item() - 0.2510   ) < 1e-3

			def    lowerCAmelCase__						(							self			:							str   ):
							UpperCamelCase_:				Dict						=    self.scheduler_classes[0]
							UpperCamelCase_:				Tuple						=    self.get_scheduler_config()
							UpperCamelCase_:				List[Any]						=    scheduler_class(**snake_case_   )

							UpperCamelCase_:				str						=    [106, 0]
							scheduler.set_timesteps(timesteps=snake_case_   )
							UpperCamelCase_:				int						=    scheduler.timesteps

							UpperCamelCase_:				List[Any]						=    torch.manual_seed(0   )

							UpperCamelCase_:				List[Any]						=    self.dummy_model()
							UpperCamelCase_:				int						=    self.dummy_sample_deter * scheduler.init_noise_sigma

							for t in timesteps:
											# 1. scale model input
											UpperCamelCase_:				Optional[int]						=    scheduler.scale_model_input(snake_case_    ,						snake_case_   )

											# 2. predict noise residual
											UpperCamelCase_:				Dict						=    model(snake_case_    ,						snake_case_   )

											# 3. predict previous sample x_t-1
											UpperCamelCase_:				str						=    scheduler.step(snake_case_    ,						snake_case_    ,						snake_case_    ,						generator=snake_case_   ).prev_sample

											UpperCamelCase_:				List[str]						=    pred_prev_sample

							UpperCamelCase_:				Union[str, Any]						=    torch.sum(torch.abs(snake_case_   )   )
							UpperCamelCase_:				str						=    torch.mean(torch.abs(snake_case_   )   )

							assert abs(result_sum.item() - 347.6357   ) < 1e-2
							assert abs(result_mean.item() - 0.4527   ) < 1e-3

			def    lowerCAmelCase__						(							self			:							str   ):
							UpperCamelCase_:				int						=    self.scheduler_classes[0]
							UpperCamelCase_:				Optional[int]						=    self.get_scheduler_config()
							UpperCamelCase_:				Tuple						=    scheduler_class(**snake_case_   )

							UpperCamelCase_:				Any						=    [39, 30, 12, 15, 0]

							with self.assertRaises(snake_case_    ,						msg="""`timesteps` must be in descending order."""   ):
											scheduler.set_timesteps(timesteps=snake_case_   )

			def    lowerCAmelCase__						(							self			:							str   ):
							UpperCamelCase_:				Union[str, Any]						=    self.scheduler_classes[0]
							UpperCamelCase_:				Tuple						=    self.get_scheduler_config()
							UpperCamelCase_:				List[str]						=    scheduler_class(**snake_case_   )

							UpperCamelCase_:				Tuple						=    [39, 30, 12, 1, 0]
							UpperCamelCase_:				List[Any]						=    len(snake_case_   )

							with self.assertRaises(snake_case_    ,						msg="""Can only pass one of `num_inference_steps` or `timesteps`."""   ):
											scheduler.set_timesteps(num_inference_steps=snake_case_    ,						timesteps=snake_case_   )




			def    lowerCAmelCase__						(							self			:							int   ):
							UpperCamelCase_:				Any						=    self.scheduler_classes[0]
							UpperCamelCase_:				int						=    self.get_scheduler_config()
							UpperCamelCase_:				int						=    scheduler_class(**snake_case_   )

							UpperCamelCase_:				int						=    [scheduler.config.num_train_timesteps]

							with self.assertRaises(
							    snake_case_    ,						msg="""`timesteps` must start before `self.config.train_timesteps`: {scheduler.config.num_train_timesteps}}"""    ,						):
											scheduler.set_timesteps(timesteps=snake_case_   )






